On the limits of machine learning-based test: a calibrated mixed-signal system case study

Abstract : Testing analog, mixed-signal and RF circuits rep- resents the main cost component for testing complex SoCs. A promising solution to alleviate this cost is the machine learning- based test strategy. These test techniques are an indirect test approach that replaces costly specification measurements by simpler signatures. Machine learning algorithms are used to map these signatures to the performance parameters. Although this approach has a number of undoubtable advantages, it also opens new issues that have to be addressed before it can be widely adopted by the industry. In this paper we present a machine learning-based test for a complex mixed-signal system –i.e. a state-of-the-art pipeline ADC– that includes digital calibration. This paper shows how the introduction of digital calibration for the ADC has a serious impact in the proposed test as calibration completely decorrelates signatures from the target specification in the presence of local mismatch.
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Communication dans un congrès
Proceedings of DATE 2017, Mar 2017, Lausanne, Switzerland
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http://hal.univ-grenoble-alpes.fr/hal-01432807
Contributeur : Manuel Barragan <>
Soumis le : jeudi 12 janvier 2017 - 10:29:46
Dernière modification le : jeudi 11 janvier 2018 - 06:15:44

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  • HAL Id : hal-01432807, version 1

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Manuel Barragan, Gildas Leger, Gines Antonio, Peralias Eduardo, Rueda Adoracion. On the limits of machine learning-based test: a calibrated mixed-signal system case study. Proceedings of DATE 2017, Mar 2017, Lausanne, Switzerland. 〈hal-01432807〉

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